ryan m. bright with contributions from: francesco cherubini * , anders h. str ømman * , edgar g....

1

Ryan M. Bright

With Contributions From:Francesco Cherubini *, Anders H. Strømman *, Edgar G.

Hertwich *, Glen P. Peters†, Terje Berntsen†2

*Industrial Ecology Program, Energy and Process Engineering, NTNU, Trondheim, Norway

† Center for International Climate and Environmental Research – Oslo (CICERO), Norway

2Department of Geosciences, University of Oslo, NorwayWorkshop on “Mitigation of CO2 emissions by the agricultural sector “, Bergen, October 3, 2011

The Importance of Forestry in Mitigating Climate Change

2

Program for Industrial Ecology at NTNU

Professor Edgar Hertwich, Director Associate Professor Anders Hammer

Strømman Life Cycle Assessment Environmental Input-Output Analysis

Ongoing Research Activities: Renewable & fossil energy systems:

Bioenergy, Wind power CCS Road transport, Electric mobility, Batteries Biochemicals, Materials, Agriculture Sustainable consumption, emissions embodied

in trade, emissions from international trade and transport

3

Bioenergy and Fuels Group Associate Professor Anders Hammer

Strømman FME CenBio (UMB, NTNU, SINTEF, S&L…) ClimPol (S&L,UMB, NTNU, CICERO)

Francesco Cherubini, PhD LCA of Bioenergy, -fuels, -chemicals and GHG

metrics Ottar Michelsen, PhD

LCA of Bioenergy and Biodiversity Ryan Bright, PhD Candidate

LCA of Biofuels and –energy, Climate impacts Geoffrey Guest, PhD Candidate

LCA of Bioenergy systems

4

Agenda

I. Forests and Climate: FundamentalsII. The Boreal Forest and Climate:

Literature SurveyIII.Case Study: Norwegian Forest

BiofuelsIV. Conclusions

5 Forests and Climate: Carbon Cycle

Source: Bonan, Science, (2008)

Via photosynthesis, forests act as a carbon sinks, reducing atmospheric CO2 concentrations by storing carbon in above & below ground living biomass, soil, litter, & dead wood pools

Human disturbance, i.e., forest management activities, can either enhance or reduce the C-sink capacity of forests For example, by changing rotation lengths,

silviculture practice, species composition – or by simply planting on new areas.

How to measure impacts from forest bioenergy?

6 Forests and Climate: Carbon Cycle

Source: Bonan, Science, (2008)

Time-integrated emission metrics like GWP pose challenges for attributional type assessments (LCA, Carbon Footprint, etc.) of bioenergy sourced from slow-growth boreal forest biomass Are CO2 from biomass combustion climate neutral?

7

CO2 From Biomass

B

Time, years

Abov

e gr

ound

ca

rbon

sto

ck

Rotation period, r

A

X CO2 out

Atmosphere

Cherubini et al., 2011a

8 CO2 in the Atmosphere during Regrowth

B

Time, years

Abov

e gr

ound

ca

rbon

sto

ck

Rotation period, r

A

X CO2 out

Atmosphere

Warming during Regrowth

Cherubini et al., 2011a

9 Forests and Climate: Carbon Cycle

Source: Bonan, Science, (2008)

2 accounting approaches: 1. Scenario analysis Use forest models to account for the

net atmospheric CO2 flux in time of some bioenergy scenario compared to a reference (shadow) scenario Holtsmark, (2011); Bright et al., (2011)

2. Incorporate the effective forest carbon sink into the global carbon cycle via a modified Impulse Response Functions (IRF) Cherubini et al. (2011a, b)

10

Both biogenic and anthropogenic CO2 emissions cause a perturbation to atmospheric CO2 concentrations

This perturbation is modeled with an Impulse Response Function (IRF) to simulate interactions among the atmosphere, the oceans, and the terrestrial biosphere

An IRF essentially gives us the time profile of CO2 in the atmosphere

The IRF in IPCC 4AR is for anthropogenic CO2 and is based on the Bern2.5 carbon cycle-climate model

The Bern2.5 IRF does NOT contain formulations to represent constant stock forests in rotation In other words, using it does not allow us to assess radiative forcing

impacts of bioenergy sourced from biomass managed in rotation cyclesCherubini et al., 2011a

11

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 20 40 60 80 100 120 140

Anthropogenic CO2

Estimating New IRFs for Biogenic CO2

Atm

osph

eri

c Fr

acti

on

Time (Year)

Cherubini et al., 2011a

12

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 20 40 60 80 100 120 140

Anthropogenic CO2 Annual crops (r=1 year)

Estimating New IRFs for Biogenic CO2

Atm

osph

eri

c Fr

acti

on

Time (Year)

Cherubini et al., 2011a

13

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 20 40 60 80 100 120 140

Anthropogenic CO2 Annual crops (r=1 year) Poplar (r=20 years)

Estimating New IRFs for Biogenic CO2

Atm

osph

eri

c Fr

acti

on

Time (Year)

Cherubini et al., 2011a

14

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 20 40 60 80 100 120 140

Anthropogenic CO2 Annual crops (r=1 year) Poplar (r=20 years) Birch (r=60 years)

Estimating New IRFs for Biogenic CO2

Atm

osph

eri

c Fr

acti

on

Time (Year)

Cherubini et al., 2011a

15

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 20 40 60 80 100 120 140

Anthropogenic CO2 Annual crops (r=1 year) Poplar (r=20 years) Birch (r=60 years) Spruce (r=100 years)

Estimating New IRFs for Biogenic CO2

Atm

osph

eri

c Fr

acti

on

Time (Year)

Cherubini et al., 2011a

16

Rotation VIRF FIRFr GWPbio GWPbio GWPbio GWPbio GWPbio GWPbio

(years) TH = 20 TH = 100 TH = 500 TH = 20 TH = 100 TH = 5001 0.04 0.01 0.00 0.02 0.00 0.00

10 0.38 0.11 0.03 0.22 0.04 0.0120 0.75 0.22 0.07 0.47 0.08 0.0230 1.05 0.32 0.10 0.68 0.12 0.0240 1.21 0.43 0.13 0.80 0.16 0.0350 1.30 0.54 0.16 0.87 0.21 0.0460 1.35 0.64 0.20 0.90 0.25 0.0570 1.37 0.75 0.23 0.93 0.30 0.0580 1.39 0.86 0.26 0.94 0.34 0.0690 1.41 0.96 0.29 0.95 0.39 0.07

100 1.42 1.05 0.33 0.96 0.43 0.08

Annual crops

Fast growing biomass

Tropical forest

Temperate forest

Boreal forest

2

2

2

2

00

2

00

.

TH

CObioCO

bio THCO

CO

C f t dtAGWP

GWP kgCO eqAGWP

C y t dt

Biogenic CO2

emissions are treated as the other GHGs

GWPs for Biogenic CO2

Cherubini et al., 2011a

17 Forests and Climate: Carbon Cycle Alternatively, one could avoid emission metrics with

fixed time horizons altogether:

where ∆ECO2(t) is the net flux change attributed to the bioenergy scenario after summing removals and emissions from all forest carbon pools plus non-biomass based emissions

2 2 2 2 20

( ) ( ') ( ') ( ')d 't

Scenario BAUCO CO CO CO CORF t k E t E t y t t t

18

Forests and Climate

In addition to altering the C-balance and emissions of other GHGs, planting new forests or changing management practice in existing forests come with an additional suite of biophysical changes

Source: Bonan, Science, (2008)

19

Forests and Climate

QN + QH + QLE + QG = W/m2, whereQN = QS(1-Albedo) + ↓QLW - ↑QLW

QH

QLE

↓QL

W

↑QL

WAlbedo

QS

QG

NomenclatureQN = Net radiative flux at top of atmosphereQH = Turbulent sensible heat fluxQLE = Turbulent latent heat flux (evaporation/transpiration)QG = Heat flux into Earth’s surfaceQS = Solar irradiance↓QLW = Downward atmospheric irradiance↑QLW = Upward surface irradiance

Blue = Non-radiative land use forcing variablesRed = Radiative forcing variables

Indirect effect

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Forests and Climate

∆QN + QH + QLE + QG = ∆W/m2, where∆QN = QS(1-∆Albedo) + ↓QLW - ↑QLW

QH

QLE

↓QL

W

↑QL

W∆Albedo

QS

QN

QG

NomenclatureQN = Net radiative flux at top of atmosphereQH = Turbulent sensible heat fluxQLE = Turbulent latent heat flux (evaporation/transpiration)QG = Heat flux into Earth’s surfaceQS = Solar irradiance↓QLW = Downward atmospheric irradiance↑QLW = Upward surface irradiance

Blue = Non-radiative land use forcing variablesRed = Radiative forcing variables

A change (“∆”) to any variable due to a land surface change induces a climate forcing in W/m2

Indirect effect

21

Forests and Climate: From Local to Global

Evaporative cooling decreases local surface air temps by increasing the ratio of transmitted surface latent heat to sensible heat flux (Bowen ratio)

But heat in atmosphere is distributed and returns to the surface elsewhere, thus there cannot be a global warming from this effect

Ban-Weiss et al., Env. Res. Lettr., (2011): Simulated the effects on global climate of increased surface latent heat fluxes from increased evaporation

Report a global cooling due to increased cloud albedo due to increased evaporation, i.e., changes in local hydrologic cycles can indirectly effect global climate

22 Forests vs. Grass/Croplands: Direct Biogeophysical Climate Effects Grass/cropland’s

higher reflectivity (albedo) cools surface air temp.’s relatively more than forests

In contrast, forests often evaporate more water and transmit more heat to the atmosphere, cooling it locally compared to (unirrigated) cropland

More water in atmosphere leads to greater number and height of clouds leading to more rainfall…indirect climate effects

Source: Jackson et al., Env. Res. Lett., (2008)

23

Boreal Forest Change & Climate Change

Bala et al., PNAS (2007): Simulation Conversion of all forested areas to cropland areas in boreal regions:

Increases in both surface and TOA albedo dominate over decreases in evapotranspiration (latent heat fluxes), leading to net cooling

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Swann et al., PNAS, (2010): Simulation Deciduous forest expansion in northern boreal regions:↓ Surface albedo & ↑ Transpiration leads to

upper atmosphere radiative imbalances and a net regional warming effect, thus triggering positive snow/ice melt feedbacks

Boreal Forest Change & Climate Change

25

Assessing Boreal Forest Change and Climate Impact

Full assessments of land surface-atmosphere interactions require sophisticated climate models with interactive terrestrial biospheresPielke Sr. et al., (2002) Impacts from changes

to the hydrologic cycle and surface energy budget cannot be quantified in terms of a radiative forcing (i.e., “QG, QLE, QH”)

However, surface albedo change can be expressed using radiative forcing as a metric for comparison with emissions

26

Betts (2000); Claussen et al., (2001); Sitch et al., (2005); Bala et al., (2007); Bathiany et al. (2010)Global simulations of hypothetical land

use changes whereby whole latitude bands of homogenous forest cover were replaced by crop/grasslands

Radiative forcing from albedo changes often dominate over carbon cycle change forcings

Why?

Boreal Forests and Climate: Albedo, Carbon Cycle, and Land Use Change

27

Importance of Snow Albedo in High Latitude Regions

Coniferous forests reflect less incoming shortwave radiation

Snow albedo (αs) on non-forested areas >200% higher

Albedo changes from afforestation are significant when snow is present

Betts, Nature, (2000)

28 “Carbon-only” approach can overestimate climate benefit of forestation policy

“Failure to account for albedo forcing (“EESF”) may have consequences that are potentially at odds with the aims of climate change mitigation” Betts, Nature, (2000); Betts, Tellus B, (2007)

Betts, Nature, (2000)

29

Pongratz et al., (2011): Quantified albedo and carbon cycle forcings from actual historical land use changes in boreal regionsFindings contrast with Betts (2000); Claussen et al.,

(2001); Sitch et al., (2005); Bala et al., (2007); Bathiany et al. (2010)

CO2 forcings dominate over albedo forcings Actual land cover changes in the past occurred

preferentially in places more suitable for agriculture, i.e., lands with higher carbon turnover (productivity) and lower relative annual snow cover

Region-specificity important!

Boreal Forests and Climate: Albedo, Carbon Cycle, and Land Use Change

30

Assessing Climate Impacts of Norwegian Forest Policy

Emissions Forcing Temperature Sea level rise, etc.Where do we measure?

Impact assessments need clearly defined temporal and spatial boundariesWhere will land use perturbations actually

occur?Instantaneous impacts? Impacts over time?Local vs. global impacts? Impacts from forest sector only direct or from all

anthropogenic activities affected? What about C-cycle and albedo dynamics? Will

forest carbon sinks and albedo changes be affected equally over the same time scale?

31

Main Research Question Given a scenario in which ~6.7 Mm3/yr. of

additional timber is felled and directed towards the production of transportation biofuel in Norway for the next 100 years: How important is a changing forest albedo for the

assessment of climate impacts of Norwegian (boreal) forest biofuels?

32 9-Yr. Monthly Surface Albedo (w/snow albedo) MODIS satellite, high-quality, cloud free data only

Winter albedo of surrounding Open Shrub areas in sample forestry regions 150-200% higher

Bright et al., 2011

33 Radiative Forcing from Albedo Change

Time evolution of surface albedo on harvested areas due to regrowth is based on time series information on canopy closure and Leaf Area Index (LAI) Important drivers for albedo change in managed boreal forests! (Nilson &

Peterson, 1994; 1999) Instantaneous forcing over time is the difference in TOA SW* over time

for the two scenarios:

Albedo, cloud, and optical data then fed into a radiative transfer model

( ) * ( ) * ( )Biofuel BAURF t SW t SW t

Bright et al., 2011

34

Global Forcing

∆Land Use Sector

Emission

∆Transport Sector Emission∆Albedo

Net ∆

Min. Albedo Uncertainty Scenario

Max. Albedo Uncertainty Scenario

Monthly albedo uncertainty (2 x σ) for the 9-yr. data sample is used to derive upper and lower estimates for mean annual local SW*; “Max Albedo Uncertainty Scenario” = max ∆α; “Min Albedo Uncertainty Scenario” = min ∆α

BAU

Net biogenic CO2 flux, modeled with anthropogenic CO2 IRF (since effective forest sinks are included in the forest model for BAU and Biofuel scenarios)

Bright et al., 2011

35

Tim-Integrated Forcing (iRF)

∆LU Sector

Emission

∆Trnspt. Emission∆Albedo

Net ∆

Min. Albedo Uncertainty Scenario

Max. Albedo Uncertainty Scenario

BAU

Bright et al., 2011

36 What about other energy services? ALTERNATIVE SCENARIO: HOME HEAT (~80

PJ-dry pellets/yr.) Biomass chips converted to pellets at ƞ=94%, pellet stoves

substitute LFO boiler, at "residential home"

Bioenergy Supply: Fuel chain emission/TJ Fuel at final user

*Time-dependent LU sector CO2; *Fossil CO2

RESIDENTIAL HEATWood Pellets

LFO

kg-CO2/TJ 9-13,000* + 9,180*

10,100

kg-N2O/TJ 0.7 0.2kg-CH4/TJ 19.3 41.1Data Source:

NTNU Ecoinvent, Avg. Europe

Bioenergy Use: Fuel-dependent emission/TJ Fuel combustedRESIDENTIAL

HEATWood Pellets

LFO

kg-CO2/TJ

87,500 77,400

kg-N2O/TJ

4 0.6

kg-CH4/TJ

30 10

Data Source:

IPCC IPCC

37 CO2-eq. Substitution Efficiency BE SCENARIO 1: ROAD TRANSPORT (Diesel)

ƞCO2-eq. = 27-30% BE SCENARIO 2: HOME HEAT (LFO)

ƞCO2-eq. = 83-88%

Life cycle emissions (direct + indirect GHGs) of the bioenergy product system need to be 1/ ƞCO2-eq. times lower per energy service than the fossil reference product system in order to realize a net GHG reduction benefit

38

LFO Substitution Scenario

∆Land Use Sector

Emission

∆Emissions, Net Total

∆Household Sector Emissions

Wood Pellets substitute LFO, at residential home, NorwayBAU

39

LFO Substitution Scenario

∆Land Use Sector

Emission

∆ Net Total

∆H.H. Sector Emissions

∆Albedo

Same scenario, with ∆Albedo

BAU

40

Case Study Summary Net climate benefits from albedo change occur

mostly in short-term; Long-term climate benefits mostly accrue through fossil substitution effects Efficiency matters!

Short-term forest management strategies should focus on enhancing carbon cycle and albedo benefits simultaneously in light of tradeoffs between the two Enhancing carbon sink productivity, for example, through

species switching, fertilization, etc. may negate albedo-enhancing efforts like delayed regeneration, shortened rotation periods, etc.

41

Final Summary

Impact assessments need clearly defined temporal and spatial boundaries

If emission-based metrics (GWP) are applied, carbon cycle dynamics and flux timing ought to be integrated “GWPbio”

Land use sector climate impacts require more than GHG summation Biogeophysical factors matter

Albedo change impacts are important in Norwegian forestry, particularly in the short-term

42

Moving Forward…. Many unanswered questions remain!

How do specific silviculture and management decisions affect physical properties of forests, and how do these properties affect albedo and carbon cycle developments in time?

Which specific management strategies will maximize mitigation benefits when albedo/carbon cycle tradeoffs are considered?

How will changes in other biophysical factors affecting the hydrologic cycle and the surface energy budget affect climate, both locally and globally?

How will the long-term albedo and carbon cycle dynamics be shaped by climate changes itself?

43

Thank you. More info? Contact: ryan.m.bright@ntnu.no

44

Literature Cited• Bonan, G. B. (2008). "Forests and Climate Change: Forcings, Feedbacks, and

the Climate Benefits of Forests." Science 320: 1444-1449.• Jackson, R. B., J. T. Randerson, et al. (2008). "Protecting climate with forests."

Environmental Research Letters 3: 044006 (044005pp).• Bala, G., K. Caldeira, et al. (2007). "Combined climate and carbon-cycle effects

of large-scale deforestation." PNAS 104(16): 6550-6555.• Swann, A. L., I. Y. Fung, et al. (2010). "Changes in Arctic vegetation amplify

high-latitude warming through the greenhouse effect." PNAS 107(4): 1295-1300.

• Ban-Weiss, G. A., G. Bala, et al. (2011). "Climate forcing and response to idealized changes in surface latent and sensible heat." Environmental Research Letters 6: 034032.

• Betts, R. A. (2000). "Offset of the potential carbon sink from boreal forestation by decreases in surface albedo." Nature 408: 187-190.

• Claussen, M., V. Brovkin, et al. (2001). "Biogeophysical versus biogeochemical feedbacks of large-scale land cover change." Geophysical Research Letters 28(6): 1011-1014.

• Sitch, S., V. Brovkin, et al. (2005). "Impacts of future land cover changes on atmospheric CO2 and climate." Global Biogeochemical Cycles 19: GB2013.

• Bathiany, S., M. Claussen, et al. (2010). "Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model." Biogeosciences 7: 1383-1399.

• Betts, R. (2007). "Implications of land ecosystem-atmosphere interactions for strategies for climate change adaptation and mitigation." Tellus B 59(3): 602-615.

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• Pongratz, J., C. H. Reick, et al. (2011). "Past land use decisions have increased mitigation potential of reforestation." Geophysical Research Letters: 38: L15701.

• Bright, R. M., A. H. Strømman, et al. (2011). "Radiative Forcing Impacts of Boreal Forest Biofuels: A Scenario Study for Norway in Light of Albedo." Environmental Science & Technology 45(17): 7570-7580. http://pubs.acs.org/doi/abs/10.1021/es201746b

• Nilson, T. and U. Peterson (1994). "Age Dependence of Forest Reflectance: Analysis of Main Driving Factors." Remote Sensing of Environment 48: 319-331.

• Nilson, T., A. Kuusk, et al. (2003). "Forest Reflectance Modeling: Theoretical Aspects and Applications." Ambio 32(8): 535-541.

• Cherubini, F., G. P. Peters, et al. (2011a). "CO2 emissions from biomass combustion for bioenergy: atmospheric decay and contribution to global warming." Global Change Biology Bioenergy 3(5): 413-426. http://onlinelibrary.wiley.com/doi/10.1111/j.1757-1707.2011.01102.x/abstract

• Cherubini, F., A. H. Strømman, et al. (2011b). "Effects of boreal forest management practices on the climate impact of CO2 emissions from bioenergy." Ecological Modelling In Press: http://www.sciencedirect.com/science/article/pii/S0304380011003693

• Schnute, J. (1981). "A versatile growth model with statistically stable parameters." Canadian Journal of Fisheries and Aquatic Sciences 38: 1128-1140.

• Pielke Sr., R. A., G. Marland, et al. (2002). "The influence of land-use change and landscape dynamics on the climate system: relevance to climate-change policy beyond the radiative effect of greenhouse gases." Phil. Trans. R. Soc. Lond. A 360: 1705-1719.

• Holtsmak, B., (2011). “Harvesting in boreal forests and the biofuel carbon debt.” Climatic Change In Press: DOI 10.1007/s10584-011-0222-6.

Literature Cited

46

Extras

47

Boreal Forest Management Effects on IRF

Source: McGuire et al., (2006)

48 CO2-eq.’s from Albedo Change (Betts’ method)

With average above-ground C-stock of 40 t-C/ha for 80-100-yr. Spruce (“Needleleaf”) and 20 t-C/ha for 60-80-yr. Birch (“Mixed”): “Needleleaf” “Open Shrub” = -15.8 W/m2 = -23 t-C-eq./ha = -

58% “Mixed” “Open Shrub” = -13.9 W/m2 = -20 t-C-eq./ha = -100% Single pulse, instantaneous impact only No dynamic treatment of CO2 residence time in

atmosphere Ignores albedo changes in time due to forest re-

growth

49

Annual CO2-eq.’s∆LU Sector Emission

∆Transport Sector Emission

∆Albedo

Net ∆

Min. Albedo Uncertainty Scenario

Max. Albedo Uncertainty Scenario

50

Land use simulation modeling at landscape level is employed to: Account for carbon fluxes on productive forest areas over time

for both a Fossil Reference (control) and a Biofuel scenario Track the evolution of species and age distribution on

productive forest areas over time for integration with albedo modeling, for the same two scenarios

Life cycle inventory analysis is performed to: Systematically quantify GHG emissions associated with forest

management activities and the transportation fuel production and consumption system, both scenarios

Scenario analysis enables: Attribution of net carbon-flux changes from land use to biofuel

system Attribution of net albedo changes from land use to biofuel

system Attribution of avoided life cycle fossil fuel emissions to biofuel

system Radiative forcing analysis is needed to:

Compare impacts from emission changes to that of albedo change

Integrated Analytical Framework

51

Albedo Sampling 5 Sample Regions, 1 for each major

logging region (northern Norway excluded)

Albedo data collected for 3 Sites (25 ha)/Region

Albedo data subsets overlap with IGBP Land Classification system:i. “(5) Mixed Forests” = Birch-dominant

site proxyii. “(1) Evergreen Needleleaf Forests” =

Pine/spruce-dominant site proxyiii. “(7) Open Shrub” = Clear-cut site

proxy Site selection criteria:

Forests must be mature, managed forests

100% of pixels must overlap the IGBP proxy

9-years of monthly albedo data are collected for each site

(% of total logging)

52

Albedo and SW* Change in Time After Harvest Key assumptions are needed to model Albedo

“decay” over time (∆αs (t)) following harvest Local albedo and SW* scale linearly with each other Time series developments of physical properties like Canopy

Closure and Leaf Area Index (LAI) in managed boreal forests have been shown to be the main drivers for surface albedo change (and thus SW* flux changes)

Literature data for these time series are used to represent the functional form of ∆αs (t). CC(t) and LAI(t) are both linear up until a clear saturation age, τ

These ages vary between the two parameters thus an average is adopted for τ

τ = age 38 for Mixed Forests (birch-dominant proxy) τ = age 35 for Needleleaf Evergreen (pine/spruce proxy)

Sensitivity analysis is performed to observe the effects on the albedo forcing time profile when τ is pushed forward and brought back in time

Global forcing from albedo (α) changes at time t:

53Sensitivity: Albedo Saturation Age (τ)

τ = 30, MixedF; τ = 30, NDlF

τ = 45, MixedF; τ = 40, NDlF

τ = 38, MixedF; τ = 35, NDlF

54 “The Impact of Boreal Forest Fire on Climate Warming”

Randerson et al., 2006, Science Radiative forcing analysis following a boreal

forest fire event at Donnely Flats, Alaska, USA (63oN)

Forcing agents include:

55

Radiative forcing impacts

A = Annual forcing from long-lived GHGs and the postfire trajectory of surface albedo

B = Cumulative annual radiative forcing for the different forcing agents averaged over the time since the fire (i.e., the age of the stand)

Randerson et al., (2006), Science

56

Actual albedo changes• Randerson et al., (2006), Science

57

Boreal regions are warming

• +1.5-2oC local warming over boreal forested regions evident over past ~50 years

Source: McGuire et al., (2006)